Game Ai

Opening — Why this matters now The game industry has flirted with large language models long enough to know the problem: they are eloquent, expensive, unreliable roommates. They forget the rules of your world, insist on internet access, and send your cloud bill straight into the end‑credits. This paper arrives with a blunt counterproposal: stop trying to cram narrative intelligence into giant, generalist LLMs. Instead, carve intelligence into small, specialized, aggressively fine‑tuned models that live locally, obey the game loop, and shut up when they’re not needed. ...

Opening — Why this matters now Agentic AI has entered its Minecraft phase again. Not because blocks are trendy, but because open-world games remain one of the few places where planning, memory, execution, and failure collide in real time. Yet most agent benchmarks still cheat. They rely on synthetic prompts, privileged world access, or oracle-style evaluation that quietly assumes the agent already knows where everything is. The result: impressive demos, fragile agents, and metrics that flatter models more than they inform builders. ...

Opening — Why this matters now For years, game AI has been split between two extremes: brittle rule-based scripts and opaque reinforcement learning behemoths. Both work—until the rules change, the content shifts, or players behave in ways the designers didn’t anticipate. Pokémon battles, deceptively simple on the surface, sit exactly at this fault line. They demand structured reasoning, probabilistic judgment, and tactical foresight, but also creativity when the meta evolves. ...